LIST OF PANELS

In a situation of enduring public sector austerity and New Public Management hypes on the one hand, and a confluence of crises on the other hand, from COVID-19 and energy shortages to climate change and environmental degradation, demands for accelerating the digital transformation of government and public administration are mounting. Against this backdrop, public sector actors vest artificial intelligence technologies (AITs), automated decision making (ADM) based on algorithms, and other data-intensive policy strategies with high-flying promises of increased accuracy, speed, precision and quality in designing and delivering public services, from welfare to security (Veale and Brass 2019). The European Commission, for example, justifies its massive investments for AITs in health care and transportation as a matter of both green growth and the cost-effectiveness of public interventions (European Commission 2018).

Public uses include chat bots at user interfaces with public services, automated case work to detect social security or tax fraud, smart diagnostics and ‘nudging’ in healthcare, predictive policing, biometric identification or dialect recognition at the ‘smart’ border or in the processing of asylum claims (Henman 2020; Jeandesboz 2021; Kaufmann, Egbert, and Leese 2019; Prainsack 2020). Beyond sector-specific applications, policies of establishing integrated digital information systems on national or European level, such as national health spaces (Hoeyer 2019, Aula 2019), the European Health Data Space (European Commission 2022), or national twin systems of public infrastructures (Braun, Kropp, Boeva forthcoming), promise to unlock social, economic and scientific value in mutually beneficial ways.

Thus, public uses of AI, big data, and data-driven automation arguably feature a ramped-up version of the classical modern idea of a "technological fix" for complex social issues, promising more profound and far-reaching solutions to a broader range of problems, including those that may not even have been specified today and that data-based prediction can identify. This techno-solutionism also comes with demands for more and better data, investment in infrastructure, and public-private partnerships for tech development and deployment.

At the same time, critical policy studies and science and technology studies have long questioned such hyper-modernist beliefs and promises and pointed to their potentially catastrophic effects (Scott 1998). Criticism refers to the underestimation or denial of technical flaws; to illegal and discriminatory effects of using big data-based technology in the public sector – from the Australian robodebt scandal to exam grade predictions during the COVID pandemic in British secondary schools both of which disproportionately penalized minority and black populations –; but also the detrimental implications for the rule of law and democratic governance (often with a focus on how technology jeapardizes normative precepts such as privacy, non-discrimination, or explicability and transparency of decisions) (overview of the debate in Paul 2022; Paul, Carmel and Cobbe forthcoming). Work on surveillance capitalism (Zuboff 2019) and the transformation of statehood vis-à-vis highly concentrated corporate power of big tech players (Fourcade and Gordon 2020; Pistor 2020) warns against the loss of public sector sovereignty and decline of democratic governance.

Against this backdrop of concurring techno-solutionist utopias and dystopias, scholarly work on AITs and data-based policy solutions has slowly emerged over the past 2-3 years. The emerging field is dominated by ethicists' and legal scholars' articulations of concrete regulatory principles and design standards to mitigate the harmful effects (for a large debate: Yeung 2019), or (to a lesser extent) rational choice propositions of proportionate governance frameworks which could mitigate risks while enabling technological innovation (e.g. Krafft et al. 2021). However, both the applied ethics and the rational choice take on AITs and data-intensive public decision-making fail to capture the politics of regulating and inserting these technologies in the public sector.

So far, less systematic attention is paid to the ongoing political struggles over developing, procuring and deploying AITs and data-intensive applications in the public sector, including to emergent approaches to regulating them (but see: Justo-Hanani 2022). We are yet to establish systematic - including comparative - insights on how the development and deployment of AIT and data-based approaches in government and policymaking interact with the ways policymaking is understood, organized, and performed by different actors and in different contexts, and how the insertion of data-intensive tech applications transform the relationships between the public sector, citizens, and the private sector.

To explore the politics of techno-solutionism in this sense, this panel asks three related questions:

1) How do techno-solutionist framings of AITs and data-intensive policies emerge in different contexts? How are they mobilized and contested, by whom, and on which basis?

2) How are AITs and data-intensive forms of decision-making appropriated in specific contexts and how does such translation work vary by sector, country, agency etc.?

3) How do dominant interpretations of AITs and data-intense decision-making in the public sector interact with issues of digital sovereignty, data justice, but also global environmental justice?

We welcome papers which address one or several of these questions in a theoretically informed manner and/or through empirical analyses. This could include already widely discussed policy domains - such as policing or border control - but also sectors we know less about (health care, social service, social care, or education). Comparative work across countries, organizations, use cases, and policy sectors is welcomed.  We also welcome work that discusses the methodological challenges which the arrival of AITs and data-intensive policy-making bring to the discipline of policy analysis.

We aim to run an in-person panel in Toronto.

Key reference points for authors to frame their papers could include:

Braun, K., C. Kropp and Y. Boeva (forthcoming). From Digital Design to Data-Assets: Competing Visions, Policy Projects and Emerging Arrangements of Value Creation in the Digital Transformation of Construction. In: Historical Social Research. Special Issue "Digital Transformation(s). On the Entanglement of Long-Term Processes and Digital Social Change", J. Hergesell, S. Büchner and J. Kallinikos (eds.).

Fourcade, Marion, and Jeffrey Gordon. 2020. “Learning Like a State: Statecraft in the Digital Age.” Journal of Law and Political Economy 1 (1): 78–108.

Henman, Paul. 2020. “Improving Public Services Using Artificial Intelligence: Possibilities, Pitfalls, Governance.” Asia Pacific Journal of Public Administration 42 (4): 209–221. doi:10.1080/23276665.2020.1816188.

Justo-Hanani, Ronit. 2022. “The Politics of Artificial Intelligence Regulation and Governance Reform in the European Union.” Policy Sciences 55 (1): 137–159. doi:10.1007/s11077-022-09452-8.

Niklas, Jedrzej, and Lina Dencik. 2021. “What Rights Matter? Examining the Place of Social Rights in the EU’s Artificial Intelligence Policy Debate.” Internet Policy Review 10 (3). doi:10.14763/2021.3.1579.

Paul, Regine. 2022. “Can Critical Policy Studies Outsmart AI? Research Agenda on Artificial Intelligence Technologies and Public Policy.” Critical Policy Studies 16(4): 497-509. https://doi.org/10.1080/19460171.2022.2123018

Paul, R., E. Carmel, and J. Cobbe. forthcoming. “Public Policy and Artificial Intelligence: Vantage Points for Critical Inquiry.” In Handbook on Public Policy and Artificial Intelligence, edited by R. Paul, E. Carmel, and J. Cobbe, 1-24. Cheltenham: Edward Elgar.

Pistor, Katharina. 2020. “Statehood in the Digital Age1.” Constellations 27 (1): 3–18. doi:https://doi.org/10.1111/1467-8675.12475.

Prainsack, Barbara. 2020. “The Political Economy of Digital Data: Introduction to the Special Issue.” Policy Studies 41 (5): 439–446. doi:10.1080/01442872.2020.1723519.

Redden, Joanna, Lina Dencik, and Harry Warne. 2020. “Datafied Child Welfare Services: Unpacking Politics, Economics and Power.” Policy Studies 41 (5): 507–526. doi:10.1080/01442872.2020.1724928.

Ulnicane, Inga, William Knight, Tonii Leach, Bernd Carsten Stahl, and Winter-Gladys Wanjiku. 2020. “Framing Governance for a Contested Emerging Technology: Insights from AI Policy.” Policy and Society, First Online: 1–20. doi:10.1080/14494035.2020.1855800.

Veale, Michael, and Irina Brass. 2019. “Administration by Algorithm? Public Management Meets Public Sector Machine Learning.” In Algorithmic Regulation, edited by Karen Yeung and Martin Lodge. Oxford, New York: Oxford University Press.

Yeung, Karen. 2019. “Why Worry about Decision-Making by Machine?” In Algorithmic Regulation, edited by Karen Yeung and Martin Lodge, 21–48. Oxford, New York: Oxford University Press.